1. Field of the Disclosure
The present disclosure relates to a radar apparatus and a corresponding radar method. Further, the present disclosure relates to a processing apparatus and method for use in such a radar apparatus and method, respectively. Still further, the present disclosure relates to a computer program for implementing said processing method and to a computer readable non-transitory medium storing such a computer program.
2. Description of Related Art
The range resolution of frequency modulated continuous wave (FMCW) radar systems improves (gets finer) by increasing the bandwidth of the transmitted chirp. Conventionally, the information about the range of the targets is extracted with a Fast Fourier Transform (FFT) of the received sampled data. Although the FFT is computationally efficient, it provides poor range resolution. Moreover, this technique hardly achieves the theoretical range resolution.
Some signal processing techniques have been adopted to achieve super-resolved range profiles of targets compared with conventional Fourier transform for the same frequency bandwidth, although the computational complexity of such methods is much larger than the FFT. They are known as spectral estimation methods and are based upon the estimation of the density of power in narrow spectral bands (bins). There are two different types of methods: non-parametric and parametric. Non-parametric methods make no assumption on the data while parametric methods use an assumed model of the data and try to estimate the parameters in that model. Parametric methods outperform non-parametric methods if the data satisfies the assumed model/structure, i.e. model postulated on data is appropriate; otherwise, non-parametric methods provide better spectral estimates than parametric methods. Some of these non-parametric methods are the periodogram, the Blackman-Tuckey method, the Bartlett method or the Welch method as described in Erman Özedemir, “Super-resolution spectral estimation methods for buried and through-the-wall object detection”, Master Thesis, Bo{hacek over (g)}aziçi University, 2008. Among the parametric methods the Yule-Walker method, the least-square method, the Matrix Pencil method as described in Zoran A. Mariievi C., Tapan K. Sarkar, Yingbo Hua and Antonije R. DjordjeviC, “Time-Domain measurements with the Hewlett-Packard Network Analyzer HP 8510 Using the Matrix Pencil Method”, IEEE transactions on microwave theory and techniques, vol. 39, no. 3, March 1991, the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) or the Multiple Signal Classification (MUSIC) method as described in the above cited Master Thesis of Erman Özedemir.
Peng Wang et al., “FMCW Radar Imaging with Multi-channel Antenna Array via Sparse Recovery Technique”, 2010 International Conference on Electrical and Control Engineering, 25-27 Jun. 2010, pp. 1018-1021 discloses a radar system composed of a single transmitter and M receiving channels. Radar echo signals are acquired to estimate the angle, range and velocity in a multiple moving target scenario. The described algorithm is based on sparse recovery technique by exploiting the sparseness of the targets in angle-range domain. It is shown in simulations for automotive scenario that the proposed algorithm yield better performance in terms of both imaging accuracy and multiple-target resolution compared with the methods of conventional beam forming and minimum variance (Capon) beam forming.
The “background” description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventor(s), to the extent it is described in this background section, as well as aspects of the description which may not otherwise qualify as prior art at the time of filing, are neither expressly or impliedly admitted as prior art against the present invention.